LAPS-ML (LAser Pulse Shaping using Machine Learning)

This repository consist on notebooks and python modules used to model laser pulse shapes from the Galadriel facility using Machine Learning.

It can be used to train different machine learning models using existing experimental data.

The project is still in development and it is NOT ready for production. Notebooks used for data analysis, modeling and predictions are currently being saved here for convinience.

Name		Name	Last commit message	Last commit date
data_utils		data_utils
models		models
.gitignore		.gitignore
README.md		README.md
analysis_cam_phy_240828.ipynb		analysis_cam_phy_240828.ipynb
analysis_cam_phy_240906.ipynb		analysis_cam_phy_240906.ipynb
analysis_cam_phy_240918.ipynb		analysis_cam_phy_240918.ipynb
analysis_camera.ipynb		analysis_camera.ipynb
backward_model_cnn.ipynb		backward_model_cnn.ipynb
build_anal_camera.ipynb		build_anal_camera.ipynb
data_explor.ipynb		data_explor.ipynb
data_explor_fifth.ipynb		data_explor_fifth.ipynb
data_explor_fourth.ipynb		data_explor_fourth.ipynb
data_explor_second_set.ipynb		data_explor_second_set.ipynb
data_explor_third.ipynb		data_explor_third.ipynb
exp-ml-250520a.ipynb		exp-ml-250520a.ipynb
exp-ml-250520b.ipynb		exp-ml-250520b.ipynb
forward_240917.ipynb		forward_240917.ipynb
forward_240918.ipynb		forward_240918.ipynb
forward_compare_nn_rf.ipynb		forward_compare_nn_rf.ipynb
forward_model_analysis.ipynb		forward_model_analysis.ipynb
gauss_fit.ipynb		gauss_fit.ipynb
gen_pulse_240918_hr.ipynb		gen_pulse_240918_hr.ipynb
good_perf_pred_240918.ipynb		good_perf_pred_240918.ipynb
good_perf_predictions.ipynb		good_perf_predictions.ipynb
goodness_analysis.ipynb		goodness_analysis.ipynb
half_size_roundtrip_model.ipynb		half_size_roundtrip_model.ipynb
init_inverse_modeling.ipynb		init_inverse_modeling.ipynb
initial_modeling.ipynb		initial_modeling.ipynb
modeling_raw.ipynb		modeling_raw.ipynb
paper_energy_gen.ipynb		paper_energy_gen.ipynb
paper_forward-2.ipynb		paper_forward-2.ipynb
paper_forward.ipynb		paper_forward.ipynb
paper_roundtrip-2.ipynb		paper_roundtrip-2.ipynb
paper_roundtrip.ipynb		paper_roundtrip.ipynb
pca.ipynb		pca.ipynb
requirements.txt		requirements.txt
roundtrip_240918_highres.ipynb		roundtrip_240918_highres.ipynb
roundtrip_model.ipynb		roundtrip_model.ipynb
roundtrip_rf_anal.ipynb		roundtrip_rf_anal.ipynb
second_modeling.ipynb		second_modeling.ipynb
test.png		test.png
test_dask.ipynb		test_dask.ipynb
test_df.ipynb		test_df.ipynb
test_dispersion.ipynb		test_dispersion.ipynb
testing_scripts-250519.ipynb		testing_scripts-250519.ipynb
testing_scripts.ipynb		testing_scripts.ipynb
vae_cond_generat.ipynb		vae_cond_generat.ipynb
vae_conditional-test.ipynb		vae_conditional-test.ipynb
vae_conditional.ipynb		vae_conditional.ipynb
var_analysis.ipynb		var_analysis.ipynb

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